Six-dimensional movable antenna (6DMA) technology has been proposed to enhance the performance of integrated sensing and communication (ISAC) systems, but its increased implementation complexity poses significant challenges due to the extensive modifications required to existing base station infrastructures. In recent years, rotatable antenna has emerged as a promising technology, offering great potential for improving both wireless communication and sensing performance by flexibly adjusting the directional antenna’s pointing. However, current research on rotatable antenna is still in its infancy, with the focus primarily on wireless communication systems, and its application has not yet been extended to ISAC systems. As a representative application of rotatable antenna, the rotatable array (RA) is more practically meaningful and implementable in engineering practice. Additionally, the sub-connected structure in hybrid beamforming, where each radio-frequency chain is connected to only a subset of antenna elements, offers higher energy efficiency and is easier to realize in practice. Given this, this paper focuses on a channel model that accounts for the efficiency of the antenna radiation pattern and studies the sub-connected hybrid beamforming design for multi-user RA-aided ISAC system, aiming to reduce hardware cost and complexity while improving communication rate and sensing accuracy. Aiming at the non-convex nature with coupled variables in this problem, this paper transforms the complex fractional objective function using the fractional programming (FP) method, and then proposes an algorithm based on the alternating optimization (AO) framework, which achieves optimization by alternately solving five subproblems. Extensive simulation results demonstrate the effectiveness of the proposed RA-aided hybrid beamforming design method. It not only significantly improves the overall system performance while reducing hardware costs, but also achieves performance comparable to that of the fully-digital beamforming design with fixed-position antennas (FPA) under specific parameter configurations.

The primary objective of jamming strategy optimization is to ensure that a jammer timely finds an effective jamming strategy against the multifunction radar (MFR), thereby ensuring the safety of targets. Deep reinforcement learning (DRL) has been widely applied in solving the problem of jamming strategy optimization. However, the process still faces challenges such as low learning efficiency and a heavy memory burden. Therefore, we propose a fast jamming strategy optimization method with imperfect experience. Firstly, we model the radar countermeasure process as a Markov decision process (MDP), and formulate the jamming reward function by combining the jamming effectiveness and the jammer’s operational intent. Secondly, we design a novel hybrid jamming strategy choice module, which uses imperfect experience to improve the optimization efficiency of jamming strategy. Furthermore, to improve sample efficiency and reduce forgetting caused by small replay buffer, we respectively employ a mixed replay buffer strategy and a knowledge consolidation technique. Finally, extensive experiments demonstrate that under the guidance of imperfect experience, our proposed method achieves faster convergence speed and higher strategy accuracy compared with existing DRL-based methods.

The aim of this article is to investigate the performance of near-field integrated sensing and communication (ISAC) systems using rotatable movable antennas (RMAs). In the proposed RMA-enabled system, the positions and rotations of antennas at the base station (BS) are dynamically adjusted to enhance both communication and sensing capabilities. Two designs are explored: 1) a sensing-centric design that minimizes the Cramér–Rao bound (CRB) with signal-to-interference-plus-noise (SINR) ratio constraints and 2) a communication-centric design that maximizes the sum-rate with a CRB constraint. To solve the formulated optimization problems, two alternating optimization (AO)-based algorithms are proposed capitalizing on the semidefinite relaxation (SDR) method and the particle swarm optimization (PSO) method. Numerical results demonstrate that: 1) the proposed rotatable MA (RMA)-enabled system outperforms the conventional fixed-position antenna and nonrotatable movable antenna (MA) systems in both sensing-centric and communication-centric designs and RMAs’ rotations show a higher performance gain in communication-centric design and 2) the proposed optimization methods achieve the Pareto boundary in both sensing-centric and communication-centric designs.

Inverse synthetic aperture radar (ISAR) images and optical images exhibit a certain degree of complementarity due to their imaging in different microwave frequency bands. To enhance the representation capability of spacecraft structures and details, we propose a novel ISAR-and-optical image fusion method based on the features of multisubbands. First, given the different imaging planes of spacecraft in ISAR and optical images, we propose an ISAR-and-optical image registration method that combines keypoint detection and homography transformation, based on the obvious geometric features of the spacecraft. Second, the multiscale decomposition of the source images is achieved by the nonsubsampled shearlet transform (NSST). Subsequently, we propose a novel activity level measurement function based on brightness, contours, and textures to achieve the fusion of low-pass subbands. Simultaneously, the texture of high-pass subbands is effectively fused based on the parameter-adaptive dual-channel pulse-coupled neural network (PADCPCNN). Finally, the fused image is obtained by the inverse-NSST. Compared to the existing state-of-the-art fusion methods, the proposed method has a better performance in both qualitative and quantitative evaluations across multiple imaging instants for different satellite models.

Read-through chimeric RNAs are being recognized as a means to expand the functional transcriptome and contribute to cancer tumorigenesis when mis-regulated. However, current software tools often fail to predict them. We have developed RTCpredictor, utilizing a fast ripgrep tool to search for all possible exon-exon combinations of parental gene pairs. We also added exonic variants allowing searches containing common SNPs. To our knowledge, it is the first read-through chimeric RNA specific prediction method that also provides breakpoint coordinates. Compared with 10 other popular tools, RTCpredictor achieved high sensitivity on a simulated and three real datasets. In addition, RTCpredictor has less memory requirements and faster execution time, making it ideal for applying on large datasets.

In mobile edge computing (MEC), users can offload tasks to nearby MEC servers to reduce computation cost. Considering that the size of offloaded tasks could disclose user location information, several location privacy-preserving task offloading mechanisms have been proposed under the single-server scenario. However, to the best of our knowledge, none of them could provide a strict privacy protection guarantee or be applicable to the multi-server scenario where the user's location can be inferred more accurately if servers collude with each other. In this paper, we propose a novel location privacy-aware task offloading framework (LPA-Offload) for both single-server and multi-server scenarios, which provides strict and provable location privacy protection while achieving efficient task offloading. Specifically, we propose a location perturbation mechanism that allows each user to perturb its real location within a rational perturbation region and provides a differential privacy guarantee. To make a satisfactory offloading strategy, we propose a perturbation region determination mechanism and an offloading strategy generation mechanism that adaptively select a proper perturbation region according to the customized privacy factor, and then generate an optimal offloading strategy based on the perturbed location within the decided region. The determination of the perturbation region could achieve personalized privacy requirements while reducing computation cost. LPA-Offload is proved to satisfy$(\epsilon,\delta)$-differential privacy, and the experiments demonstrate the effectiveness of our framework.

This paper analyzes the ergodic mutual information (EMI) of an intelligent reflecting surface (IRS)-aided multiple-input single-output system with finite-alphabet inputs relying on two- timescale channel state information. For the sake of unveiling important system design insights, asymptotic analyses are performed on the EMI in the regime of high signal-to-noise ratio (SNR). It is found that the EMI converges to some constant in the high-SNR regime and the rate of convergence is determined by the diversity order and the array gain. On this basis, two efficient algorithms are proposed to design the phase shifts of the IRS in order to improve the array gain as well as the EMI. All the analytical results are verified through computer simulations.